Senior SDET, Inference Platform
Senior SDET on Cerebras's Inference Service Quality team, owning test automation and reliability for the AI inference platform — building testbeds, validating Kubernetes deployments, CI/CD pipelines, ingress and load balancing across cloud and Cerebras hardware. Core stack: Python/Go, Kubernetes, Jenkins, NGINX.
- Compensation
- Not disclosed
- Location
- Sunnyvale, United States
- Work setup
- Hybrid
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Qa
- Posted
Skills
Must have
- 3+ years of experience in software engineering, QA/quality engineering, systems engineering, or infrastructure development
- Strong programming skills in Python and/or Go (both a plus)
- Experience building automation tools, testing frameworks, or internal developer tooling
- Hands-on experience with CI/CD systems (e.g., Jenkins)
- Experience debugging complex systems, distributed services, or networked infrastructure
- Familiarity with systems-level development, infrastructure tooling, or platform integration
- Strong problem-solving skills across multiple system and infrastructure layers
- Excellent communication and collaboration skills
- Experience mentoring junior engineers
Nice to have
- Hands-on experience with Kubernetes and container orchestration in production or staging
- Experience with cloud-managed Kubernetes such as Amazon EKS
- Experience with GitOps/deployment tooling (e.g., ArgoCD)
- Familiarity with ingress controllers, service discovery, NGINX, and load balancing
- Experience with build systems such as Bazel
- Experience with cluster tooling and operations (e.g., k9s)
- Exposure to performance debugging, profiling, or system observability tools
- Experience with ML inference infrastructure, model serving systems, or GPU-accelerated workloads
Full description
Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services.
This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation.
Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.
About the Role
We are looking for an Inference Platform SDET to join the Inference Service Quality team at Cerebras and work on the inference platform. This team sits at the intersection of distributed systems, cloud and cluster infrastructure, and the software stack that serves the world's fastest AI inference.
In this role, you will own the quality and reliability of the infrastructure that deploys and runs the Cerebras Inference Platform — from CI/CD pipelines and Kubernetes-based deployments to ingress, load balancing, and service discovery. You will validate the platform both in cloud environments and on real Cerebras clusters, working side by side with the Inference Platform development team to catch issues before our customers do.
This is an excellent opportunity for engineers who enjoy infrastructure, automation, and debugging across the full deployment stack, and who want to ensure that a platform serving inference at massive scale stays fast, reliable, and production-ready.
Responsibilities
- Design, build, and maintain test infrastructure and automation for deploying and validating the Cerebras Inference Platform.
- Validate the platform across environments — from cloud-managed Kubernetes to deployments running on Cerebras hardware.
- Test and verify deployment infrastructure including Kubernetes workloads, CI/CD pipelines, ingress and service discovery, NGINX, and load balancing.
- Collaborate closely with the Inference Platform development team to ensure new features and platform capabilities ship reliably.
- Investigate and debug complex issues spanning networking, orchestration, deployment, and distributed services.
- Develop and maintain testbeds used to validate platform performance, scalability, and reliability.
- Identify failure points, bottlenecks, and edge cases that impact platform stability and inference performance.
- Contribute to test plans and validation strategies for new platform features and releases.